
The module examines the intellectual framework of inductive inference,
learning and prediction, data modelling, data mining, and the mathematical and
algorithmic methods required for unsupervised classification
(also called clustering), for supervised classification
(e.g. using decision structures) and for other similar and related problems.
It is about
the mathematics behind the software,
the algorithms implemented in the software, and
how to write new software for inductive inference.
MML is used to combat the overfitting problem and to balance
model complexity v. the fit to the data.
 2005: Data Mining and MML (3pts)
 weeks 212, semester 1,
[lectures etc.]
(L2 Wednesday 911am)
 Results summary
for those with complete assessment as of 6/2005:
71% HD, 14% D, 14% C.
 2004: Data Mining and MML (3pts)  name shortened.
 weeks 112, semester 1,
[lectures etc.]
(L4 Monday 12.002.00pm)
 2003 Learning and Prediction I: Data Mining (3pts):
 weeks 113, semester 1, [plan]
[lectures] & prac's.
(E3 Tuesday 3pm)
 2002:
weeks 16, semester 1, [plan]
[lectures] & prac's.
 CSE454
`Learning and Prediction I: Data Mining' (3pts) and
CSE455
`Learning and Prediction II: MML Data Mining' (3pts)
together replaced
CSC423 `Learning and Prediction' (6pts).
 2001:
 See [CSE423] (6pts) 19992001.

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